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Emerging economies (EMEs) often ignore effective mitigation strategies for persistent climate changes to prioritise growth acceleration. This paper shows that they cannot sustain their economic growth due to the adverse impact of temperature rise on total factor productivity (TFP). Using a standard growth model, it demonstrates how temperature rise and variation for growing carbon emissions reduce capital productivity along with the damage to ecosystem services and labour productivity, adversely impacting total factor productivity (TFP) of an individual economy. A cross-sectional augmented auto-regressive distributed lag model (CS-ARDL), which addresses the issues of endogeneity, heterogeneity and cross-sectional dependence with stochastic trends, has been applied to 21 EMEs over the period from 1990 to 2018 and reveals a strong negative impact of temperature rise on total factor productivity. Although EMEs have heterogeneous impacts across the countries depending upon their climatic zones and income levels, one degree increase in temperature, on average, has decreased the TFP by approximately 3.22 per cent. It is much higher in the extreme climatic zones and less developed economies. • Temperature rise and its variation for raising carbon emission reduce capital productivity along with ecosystem services and labour productivity, adversely impacting total factor productivity (TFP). • The cross-sectional augmented auto-regressive distributed lag model (CS-ARDL), which addresses endogeneity, cross-sectional dependence and heterogeneity, has been applied to 21 emerging markets and developing economies for 1990 to 2018 and reveals a strong negative impact of temperature rise on total factor productivity. • One-degree increase in temperature, on average, reduces the TFP by approximately 3 per cent. • The temperature rise has heterogeneous and non-linear impacts on TFP across countries in geo-climatic locations. • Two extre-climatic zones are worse-affected than the moderate-climatic region. [ABSTRACT FROM AUTHOR]

Unprecedented climate extremes will likely become a new 'normal'. Urban political ecology is thus confronted with the challenge of exploring and theorising emerging geographies of unprecedented climate change. This raises questions on the extent and ways in which past socionatures can be mobilised for this task. It also urges a critical reflection on what forms of knowledge are needed to meet political ecology's normative aspirations of transforming emerging and future socionatures of climate change. I argue that a critique of past and present socionatures alone is not enough to meet these theoretical and normative goals. The main objective of this paper, therefore, is to lay the foundations of a future-oriented urban political ecology that approaches geographies of climate change more experimentally and speculatively. To this aim, I examine the explanatory potential of two (of many possible) experiments, collaboratively developed by a team of hydrologists, climatologists, and political ecologists. Both experiments depart from past and present logics of colonial violence, racial capitalism, and climate change, but explore competing notions of unprecedented futures. The first experiment consists of a critical-realist scenario approach that examines how power and variability in the exercise of agency might shape outcomes of future unprecedented climate extremes. The second involves a model that speculatively brings about a better world in response to climate extremes and lays the foundation of a political ecology of possibility. The paper thereby serves as a demonstration of how critique can be mobilised to explore and reimagine urban futures. [ABSTRACT FROM AUTHOR]

Taking China's provincial panel data from 2010 to 2020 as an example, this paper verifies that climate change, especially extreme climate change, can destabilize China's financial stability and explores the transmission path of climate risks with meteorological disaster losses as the channel variable. Our empirical results demonstrate that: (a) only after the extreme climate change breaks through a certain threshold, thus causing a significant impact on the financial system; (b) we cannot ignore the improvement of disaster prevention and reduction ability brought by the improvement of social and economic development level; (c) the impact of extreme climate change will spread to the financial system through the economic losses caused by meteorological disasters; (d) there is obvious regional heterogeneity in the spread of meteorological disaster losses to the financial system. Our results are robust to a wider battery of checks and have important policy implications. • This paper empirically tests the impact of climate change on financial stability. • Extreme climate change will damage China's financial stability. • The economic losses caused by climate disasters will spread to the finance. • The transmission of such losses has obvious regional heterogeneity. • This paper provides policy implication for coping with climate risks. [ABSTRACT FROM AUTHOR]

• The complex interconnectedness of climate change, energy demand, and health and long-term care poses current and future cascading challenges for the resilience of older people. • Key interconnections include a concern for the impact of frequent and intense weather events parallel to a global increase in life expectancy and social justice issues connected with strained resources and public infrastructure. • Comprehensive, targeted policies are needed on the national and global level that incentivize sustainable practices, enhance climate resilience, and support vulnerable populations in the Global South, and that mitigate the social justice implications of climate change for older adults. Challenges faced by many countries are energy insecurity, climate change, and the health and long-term care of growing numbers of older people. These challenges are increasingly intersecting with rising energy prices, aging populations, and an increased frequency and intensity of extreme climate events. This paper gives a deeper understanding of the current and predicted interconnections among these challenges through narrative-driven content and thematic analysis from workshops with a diverse group of international stakeholders from the Global North and Global South. Narratives emerged highlighting a complex nexus of interconnections and presenting critical action areas. Targeted local and global policies and interventions are needed to alleviate stress on health systems, encourage the integrated uptake of clean energy sources, and uphold social justice across all economies. Professionals can use this work to inform the design and implementation of effective interventions and increase the resilience of older adults by better preparing for systemic risks. [ABSTRACT FROM AUTHOR]

Extreme climate events are hotspots in global change. However, research on the changes in future compound events and population exposure is still limited. Leveraging from the data of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this paper aims to analyze the temporal and spatial changes of global compound temperature and precipitation extreme events in the future. We also predict the risk of population exposure and quantify the contribution of different factors to exposure. The results show that: (1) In the next 80 years, compound hot-dry event (CHDE) will increase at a rate of 0.02, 0.03, and 0.08 days per decade under the three scenarios of SSP1–2.6, SSP2–4.5 and SSP5–8.5, respectively. By comparison, compound hot-humidity event (CHHE) shows a downward trend under the three scenarios, with a downward rate of 0.01, 0.02, and 0.11 days per decade, respectively. (2) Under the SSP1–2.6 and SSP2–4.5 scenarios, CHDE and CHHE have two or more mutation points. Under the SSP5–8.5 scenario, CHDE shows a significant upward and CHHE shows a significant downward trend in the middle and late 21st century. These two indices exhibit periodic changes in all three scenarios (3) South Asia, West Asia, and Northeast Africa have higher CHDE values, while regions with higher CHHE values are located in North Asia and Greenland. (4) Climate change is a major factor affecting population exposure. For CHDE, climate, population, and their synergistic effects contribute about 75%, 20%, and 5% to the exposure, respectively. For CHHE, the contributions of these three factors are 85%, 10%, and 5% respectively. These findings provide scientific guidance for the rational formulation of population policies, the effective avoidance of climate disaster risks and the protection of human health. • Frequency of hot-dry events and hot-humidity events will increase and decrease, respectively, in the future. • There are abrupt points and periodicity in the future time series of compound temperature and precipitation extremes. • Asia is projected to have the greatest exposure and climate change contributes the most to exposure. [ABSTRACT FROM AUTHOR]

[Display omitted] • NDVI shows an overall increasing trend from 2000 to 2020. • The Hexi region shows a warming and wetting trend. • Extreme precipitation is more important for NDVI. • The lag effect of NDVI on climate extremes varies by indicators and spaces. Climate extremes significantly affects vegetation dynamics, and understanding the relationship between dryland vegetation and climate extremes is crucial for regional ecological security and disaster warning. According to meteorological and satellite remote sensing data, the paper analyses the dynamics of normalized difference vegetation index (NDVI) and climate extremes in the Hexi region from 2000 to 2020 using Theil-Sen Median, Mann-Kendall and linear regression methods; the relationship between climate extremes and vegetation dynamics is revealed using Pearson correlation and boosted regression trees (BRT) models. On this basis, the time-lag effect of NDVI in response to climate extremes was further investigated. The results show that the NDVI in the Hexi region increased from 2000 to 2020, with significant greening (67.72 %) and a small amount of degradation concentrated in the desert in the northwest and the oasis in the southeast. The correlation between climate extremes indicators and NDVI shows a large spatial heterogeneity, with extreme precipitation showing a high importance for all regions except oasis. NDVI has no lag or 1 month lag for extreme temperature, no lag for max 1-day precipitation amount (Rx1day) for areas other than mountainous areas, and a 3-month lag for max 5-day precipitation amount (Rx5day) for extensive areas. The results of this study could serve as a reference for the study of the relationship between vegetation dynamics and climate extremes in arid and semi-arid regions, and provide a theoretical basis for the establishment of scientific and reasonable strategies for regional vegetation restoration and conservation. [ABSTRACT FROM AUTHOR]

Understanding climate extremes and indices is crucial for addressing climate change impacts, including the increased variability in local weather patterns and extreme events. Previous studies on the Arabian Peninsula (AP) have been limited by sparse station data and restricted spatial coverage. To overcome this limitation, this paper aims to assess the spatial distribution and temporal trends of temperature and precipitation indices suggested by the Expert Team on Climate Change Detection and Indices (ETCCDI) using ERA5 reanalysis data from 1951 to 2020 for the AP. Additionally, this study investigated the changes relative to the reference period (1951–1980). The modified Mann-Kendall test and Sen's Slope estimator are utilized to detect significant trends and estimate their magnitude. Results revealed that ERA5 was consistent with previous studies that utilized in-situ station data, which offers a more detailed picture of the affected areas. The findings indicated a significant warming trend in temperature indices, with an increase of over 1 °C per decade observed across several areas, including the northern AP, and certain regions experiencing a discrepant change of 3 °C increase compared to the reference period. Changes in rainfall indices indicate a shift in rainfall patterns from the AP's fertile southwest regions towards more intense patterns in specific eastern regions, including Oman, Kuwait, Saudi Arabia, and Yemen. However, the precipitation temporal trends are weak in magnitude and variable spatially, with dominant decreases in both intensity (-10 mm per decade) and frequency indices (-5 days per decade). Some locations are subject to the combined effects of most heatwave indicators simultaneously, while flood indicators and yet others by drought indicators influence others. All these locations have been accurately identified in this study. The findings provide valuable information regarding the region's climate change vulnerability and adaptation needs. [Display omitted] • Prior studies relied on limited station data, short period, and MK trend test. • Extremes are assessed using ERA5, ETCCDI, Modified-MK, and Sen's Slope. • Affected areas are identified, and valuable insights for policymakers are set. • Significant warming trend: >1 °C/decade and > 2 °C change from reference period. • Spatiotemporal trends of precipitation are weak in magnitude and variable spatially. [ABSTRACT FROM AUTHOR]

Floods are a great threat to Pakistan with increasing concern. As the consequences of increased extreme weather related to climate change, Pakistan experiences severe floods almost every year. This study aims to explore and analysis the actual inundated situation, magnitude, the possible causes of the 2022 devastating floods, and future trends. We presented an enhanced nationwide flood mapping method and compared with other pixel-based image processing techniques including active contours and change detection. These algorithms were applied to Sentinel-1 Ground Range Detected (GRD) Synthetic Aperture Radar (SAR) imagery (10 m spatial resolution) with various land types and inundation scenarios in Pakistan, and were evaluated using other reference flood products. Accuracy evaluation analysis demonstrated that our algorithm has high robustness and accuracy, with the overall accuracy (OA) higher than 0.83 and critical success index (CSI) up to 0.91, and is suitable for automated flood monitoring in near real time. Nearly one-third of the lands were flooded in 2022, and more than half were inundated croplands. Punjab and Sindh provinces were the most severely affected regions, with the proportions of inundated area in 2022 (21.26% and 20.55%) nearly twice of that in 2010 (11.40% and 12.70%), indicating an intensified flooding trend. Analysis of possible influential factors showed that the intense and cumulative rainfall during the monsoon season (June to August) was the major cause of the 2022 flood event. Although the snow melted rapidly in June (the average change in snow depth is ∼10 mm), the overall ablation contributed insignificant amount to the flood water. The glacial lake outburst floods (GLOFs) induced by abnormal April–May heatwave provide water flowed into the tributaries of the Indus River, but are difficult to spread for thousands of kilometers from mountains to the plain downstream. The combination of the intrinsic arid climate and extreme floods exacerbate the already severe situation. • This paper presents an automated method based on tile thresholding and fuzzy logic. • The inundation in Punjab and Sindh in 2022 are nearly twice of that in 2010. • The cumulative rainfall is the major cause of the 2022 flood event. • The snow melting and GLOFs are likely small relative to the flood water gains. • These observations imply an intensified flooding and water cycle in Pakistan. [ABSTRACT FROM AUTHOR]

Climate change led an increase in the frequency of severe droughts, the effects of which are exacerbated by a lack of water storage. Despite increasing research on global-scale total water storage (TWS) change and drought prediction, basin-scale long-term hydrological drought, TWS change, and El Nino–Southern Oscillation (ENSO) influence in East Africa (EA) remain unexplored. In this paper, we used multiple approaches, of ensemble machine learning (ML) and weighted averaging, to conduct a basin-scale exploratory analysis of future hydrological drought and TWS change for the period 2025–2099. Climate change will affect future hydrological conditions in the basins, with maximum severity values of −90.3, −726.6, and −2567.7 Gt in the Tana, Turkana, and Victoria basins, respectively, under RCP6.0. Similarly, we found maximum severities of −187.5 and −277.7 Gt in the Abaya-Chamo and Tanganyika basins, respectively, under RCP 2.6. The most severe El Nino drought and exceptional La Nina conditions in the basins were found under both RCP scenarios. To ensure future water security in EA, sustainable water resource management strategies that can mitigate the impacts of extreme climate events in the future are urgently needed. [ABSTRACT FROM AUTHOR]